Dataset: World Happiness Report Link: https://www.kaggle.com/unsdsn/world-happiness

data(worldgeojson)
df15 <- read.csv("/home/christiano/Dropbox/trabalhos/disciplina_mineracao/datasets/datasets_menores/world-happines/2015.csv", stringsAsFactors = FALSE)
df16 <- read.csv("/home/christiano/Dropbox/trabalhos/disciplina_mineracao/datasets/datasets_menores/world-happines/2016.csv", stringsAsFactors = FALSE)
df17 <- read.csv("/home/christiano/Dropbox/trabalhos/disciplina_mineracao/datasets/datasets_menores/world-happines/2017.csv", stringsAsFactors = FALSE)

RQ1 Quais países que apresentaram as melhores evoluções e as piores quedas no score de felicidade entre 2015 e 2017

df1<-merge(df15[,c(1,3)],
           df16[,c(1,3)],
           by.x = "Country",
           by.y = "Country")
df1<-merge(df1,
           df17[,c(1,2)],
           by.x = "Country",
           by.y = "Country")
colnames(df1)<-c("Country","2015","2016","2017")
df1<-df1%>%
  mutate(rank_change=`2017`-`2015`)
asc <- df1[order(df1$rank_change),]
desc <- df1[order(df1$rank_change,decreasing = TRUE),]
dfBestChanges <- desc[1:3,]
dfWorstChanges <- asc[1:3,]
dfBestChanges
dfWorstChanges
dfBestWortC <- rbind(dfBestChanges, dfWorstChanges)
dfBestWortC <- dfBestWortC[,c(-6)]
dfRank2015 <- data.frame(country=dfBestWortC$Country,rank=dfBestWortC$`2015`,ano="2015")
dfRank2016 <- data.frame(country=dfBestWortC$Country,rank=dfBestWortC$`2016`,ano="2016")
dfRank2017 <- data.frame(country=dfBestWortC$Country,rank=dfBestWortC$`2017`,ano="2017")
dfRank <- rbind(dfRank2015,dfRank2016)  
dfRank <- rbind(dfRank,dfRank2017)  
dfRank %>% ggplot(aes(ano,rank,group=country)) + geom_line(aes(color=country)) + geom_point() + geom_label(aes(label=rank)) 

#desc[1:5,c(1,5)] %>% ggplot(aes(Country,rank_change)) + geom_bar(stat = "identity")
str(dataset)
'data.frame':   158 obs. of  12 variables:
 $ Country                      : Factor w/ 158 levels "Afghanistan",..: 136 59 38 106 25 46 100 135 101 7 ...
 $ Region                       : Factor w/ 10 levels "Australia and New Zealand",..: 10 10 10 10 6 10 10 10 1 1 ...
 $ Happiness.Rank               : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Happiness.Score              : num  7.59 7.56 7.53 7.52 7.43 ...
 $ Standard.Error               : num  0.0341 0.0488 0.0333 0.0388 0.0355 ...
 $ Economy..GDP.per.Capita.     : num  1.4 1.3 1.33 1.46 1.33 ...
 $ Family                       : num  1.35 1.4 1.36 1.33 1.32 ...
 $ Health..Life.Expectancy.     : num  0.941 0.948 0.875 0.885 0.906 ...
 $ Freedom                      : num  0.666 0.629 0.649 0.67 0.633 ...
 $ Trust..Government.Corruption.: num  0.42 0.141 0.484 0.365 0.33 ...
 $ Generosity                   : num  0.297 0.436 0.341 0.347 0.458 ...
 $ Dystopia.Residual            : num  2.52 2.7 2.49 2.47 2.45 ...

RQ2 Qual o rank dos países obtiveram a média dos melhores scores de felicidade

RQ3 Quais features influenciam no score de felicidade

test<-cor(as.matrix(df15[,-c(1,2,3)]))
test2<-cor(as.matrix(df16[,-c(1,2,3)]))
corrplot::corrplot(test,method = "square",type = "upper",mar = c(0,0,1,0))

corrplot::corrplot(test2,type = "upper",method = "square",mar = c(0,0,1,0))

RQ4

plot_ly(df16,x=~Region,
        y=~Happiness.Score,
        type="box",
        boxpoints="all",
        pointpos = -1.8,
        color=~Region)%>%
  layout(xaxis=list(showticklabels = FALSE),
         margin=list(b = 100))
n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

RQ5

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